What Is MCP (Model Context Protocol)? Explained Simply

What if you could ask Claude what your team decided in Monday’s meeting and get an answer straight from your Fireflies transcript?

No copying, pasting, or digging through notes. Once you’ve connected the two, you just ask, and Claude pulls it up for you.

But getting AI tools to access information from other apps hasn’t always been straightforward. Connecting them to your meetings, CRM, or files often meant a developer building one connection for Claude and your CRM, another for ChatGPT and your meeting notes, and so on. Add more tools, and the number of connections keeps growing.

The Model Context Protocol (MCP) is changing that. Introduced by Anthropic in November 2024, it’s an open standard that gives compatible AI tools a common way to connect with outside apps and data, instead of requiring a custom integration for every pairing.

So, what exactly is MCP, how does it work, and why are so many AI tools adopting it?

What Is MCP (Model Context Protocol)?

Model Context Protocol (MCP) is an open standard that lets AI applications connect to outside tools and data using a common set of rules. Developers can use this standard to connect AI tools with different apps without building a separate integration for every pairing. 

Think of MCP like a USB-C port for AI. Just as you can use the same type of connector for your phone, laptop, and other devices, MCP gives AI tools a common way to connect with different apps.

Fireflies, for example, is an AI teammate that joins your meetings and turns them into transcripts and notes. With your permission, its MCP server makes that information available to compatible AI tools like Claude. That’s how Claude can answer questions about your meetings without you having to copy the transcripts over.

Anthropic introduced MCP on November 25, 2024. In December 2025, it donated the project to the Agentic AI Foundation under the Linux Foundation.

MCP also stands for Microsoft Certified Partner, but that's unrelated to the AI protocol we're discussing here.

Why MCP Exists: The Integration Problem It Solves

Picture 3 AI tools and 3 business apps: a CRM, a meeting platform, and a project management tool. Connect every tool to every app, and that's potentially 9 custom integrations.

Now add a fourth AI tool. That could mean 3 more integrations, bringing the total to 12. Each one needs to be built, tested, and maintained. And if one business app changes how it works, several connections may need updating.

With MCP, the CRM provider can make its data available through an MCP server. Claude and ChatGPT can each connect to that same server, as long as they support MCP and have the right permissions. Add another compatible AI tool later, and it can use that server too.

The CRM provider still has to maintain its server, and each AI tool still needs its connection configured. But adding another compatible AI tool doesn't mean building a new custom integration from scratch.

How Does MCP Work?

MCP uses three main components.

• Host: The AI application you use, such as Claude or ChatGPT.
• Client: The connector inside the host that communicates with an MCP server.
• Server: A program that makes an external app's data or tools available to AI applications.

One host can have multiple clients, each connected to a different server. The client can find out what tools its server offers, so the AI knows which ones it can use.

Here's what happens when you use MCP to find out what your team decided in Monday's kickoff meeting.

1. You ask a question. “What did we decide in Monday's kickoff meeting?”
2. The AI chooses a tool. It identifies a tool that can search your meeting information.
3. The client sends the request. It asks the connected MCP server to find the relevant meeting details.
4. The server returns the results. It retrieves the information and sends it back through the client.
5. The AI answers you. It uses the returned information to explain what your team decided.

Tools, resources, and prompts

The search tool in that example is one of three types of capabilities MCP servers can offer.

• Tools: Functions the AI can call, such as searching meeting transcripts or creating a calendar event.
• Resources: Information an application can read, such as a document or meeting transcript.
• Prompts: Reusable instructions or templates for common tasks.

Which capabilities are available depends on the server and what the AI application supports.

What Is an MCP Server?

An MCP server is software that lets AI applications access data or use features from other apps. It can run on your computer or remotely on a server managed by a software company. You don't need a separate physical machine for every MCP server.

Here are a few examples of what different MCP servers can help AI tools do:

With the Fireflies MCP server connected to Claude, a sales manager could ask, “Which customer objections came up most often in our calls last month?” Claude can search the available meeting transcripts and use what it finds to answer the question, saving the manager from reviewing each transcript manually.

Software companies build MCP servers for their products, and developers can create them for internal tools. You can connect to an existing server without writing code if your AI application supports it. What you can access depends on the server's capabilities and your permissions.

By December 2025, the MCP ecosystem had grown to over 10,000 public servers, showing how quickly software developers were adopting the standard.

Fireflies has an official MCP server available on every plan, including Free. Want to connect your meetings to Claude? See how it works.

MCP vs. API vs. Function Calling vs. RAG

If you've been looking into AI, you've probably heard of APIs, function calling, or RAG. These terms often come up alongside MCP, but each serves a different purpose.

Here's how they compare.

MCP vs. API

APIs have been connecting apps for years. Your CRM, for instance, might use an API to share customer information with another system.

MCP doesn't replace APIs.

An MCP server can use an app's existing API to get information or carry out tasks. MCP then gives compatible AI tools a common way to use the app's data and features. Developers don't have to build a different custom integration for every AI tool that wants to use them.

MCP vs. function calling and RAG

Function calling is how an AI model asks another tool to perform a task, such as checking an order's delivery status. MCP provides a standard way for AI applications to connect with tools that can handle those requests.

RAG, short for retrieval-augmented generation, is about finding useful information before generating an answer. An AI might look through company documents for information that helps answer an employee's question.

These technologies can work together. An AI could use MCP to connect to a company's document search tool. When an employee asks about company policy, it can look up the relevant documents and answer based on what they say.

MCP, APIs, function calling, and RAG solve different problems, but they can all be part of the same workflow.

Who Supports MCP?

Anthropic created MCP, but other major AI companies have since adopted it. The protocol has caught on across the industry, with companies adding support to their AI apps and developer tools.

Some of the biggest names include:

• Anthropic: Introduced MCP in November 2024 and supports it through Claude and Claude Code.
• OpenAI: Announced MCP support in March 2025, initially through its developer tools, with support expanding to ChatGPT.
• Google: Google DeepMind announced support for MCP in April 2025, with adoption across parts of its Gemini ecosystem and developer tools.
• Microsoft: Expanded MCP support across products such as GitHub Copilot, Copilot Studio and Windows 11 in 2025, including announcements at Microsoft Build in May.

For businesses, having more AI platforms support MCP means more choice. You might prefer Claude for one task and ChatGPT for another. Both can connect to the same MCP server, provided their features and permissions allow it.

That doesn't mean every connection will work everywhere. MCP support varies by product and plan, so an MCP server available in Claude might not be accessible through your version of ChatGPT or Gemini. Check which connections your AI application supports before setting one up.

Is MCP Safe?

You can use MCP safely, but there are a few things to watch out for. How safe your connection is depends on the MCP server you use, the AI application you connect it to, and the permissions you give it.

Connecting AI to another app comes with some risks, such as:

• Untrusted servers: A poorly secured or malicious MCP server could expose sensitive company information. A malicious server might also describe its tools in a misleading way to trick an AI into using them.
• Too much access: Some MCP servers let AI tools change or delete information. Giving them unnecessary permissions increases the risk of unwanted changes.
• Prompt injection: Hidden instructions in a document or webpage could trick an AI into doing something you never asked it to do.

You can reduce these risks by treating an MCP connection like any other app you give access to your business data. Choose servers from trusted providers and check what they can access before connecting. Keep permissions limited to what you need, and use manual approval for sensitive actions when your AI application supports it.

It's also worth checking how your AI provider handles information retrieved through MCP, including whether it stores that data or uses it for model training.

If you're connecting AI to meeting transcripts or customer records, start with limited access. You can always grant more permissions later if you need them.

MCP in Action: Asking AI About Your Meetings

One way to put MCP to work is by connecting Fireflies to Claude through Fireflies' official MCP server. The server is available on every Fireflies plan, including Free, although access to connectors in Claude depends on your Claude subscription. Once they're connected, you can ask Claude about decisions made in past meetings or follow-up tasks assigned to your team, without searching through the transcripts yourself.

To get started, open Claude's connector settings and look for Fireflies. Fireflies is listed in Claude's Connectors Directory. Follow the instructions to connect your account and sign in through OAuth to approve access. You can find the full setup process in our guide on how to connect Fireflies to Claude.

You can then ask questions like, “What decisions did we make in last week's project meetings?” or “Which action items from our customer calls still need follow-up?” 

Claude uses the meeting information available through Fireflies to answer your questions. Fireflies captures and organizes your AI meeting notes, while its MCP server lets Claude access the meeting information you're authorized to share.

Want an easier way to keep track of meeting decisions and follow-ups? Fireflies keeps your meeting information organized and easy to find later.

Try Fireflies free

Frequently Asked Questions About MCP

What does MCP stand for in AI?

MCP stands for Model Context Protocol. It’s an open standard that helps AI tools connect with other apps and access their data.

Is MCP the same as an API?

No, they're different. APIs let apps share information and perform tasks, while MCP gives AI tools a common way to connect with those apps and use their features. An MCP server can use an existing API to make that happen.

Is MCP better than an API?

Not necessarily. MCP and APIs serve different purposes, and they can work together. Which one you need depends on what you're building.

Is MCP the same as function calling?

No. Function calling lets an AI model request an action from a tool. MCP provides a common way for AI applications to discover and connect to tools offered by external servers. The two can work together.

Do you need coding skills to use MCP?

Not necessarily. If your AI app supports an existing MCP connection, you can often set it up through the app's settings without writing code. Some connections need more technical setup, though, especially if you're building your own server.

Is MCP free and open source?

Yes, MCP is a free and open-source standard. Its official SDKs are available under open-source licenses, but the AI app, connected service, or MCP server you use may still charge a fee.

Can ChatGPT use MCP?

Yes, ChatGPT supports MCP, although the connections and features available to you depend on which ChatGPT product and plan you're using.

Who created MCP?

Anthropic introduced MCP in November 2024. It later donated the project to the Agentic AI Foundation under the Linux Foundation in December 2025.

Is MCP secure?

MCP can be used safely, but it depends on which server you connect to and what access you give it. Stick to trusted providers, check the permissions, and avoid giving AI tools access they don't need.

MCP: The Missing Link Between AI and Your Apps

MCP makes it easier to use AI alongside the software you already rely on. Instead of copying notes or exporting files between platforms, you can connect compatible AI tools to the information they need, provided they have the right permissions.

For everyday work, that means less time searching through old files and meeting notes. With Fireflies connected to Claude, for example, you can find past decisions and follow-ups without going through each transcript yourself.

As more software companies support MCP, you'll have more options for connecting AI to the apps your team already uses.

Want to ask Claude about your past meetings? Connect Fireflies and put your meeting history to work.